2b:null 2c:["$","title",null,{"children":"MedicaidGPT: Initial Prototype | Aaron Ge | Observable"}] 31:T4a5,function replacePlaceholders(obj, placeholderMap) { function traverseAndReplace(obj) { const regex = /^\{([^}]+)\}$/ for (const key in obj) { const value = obj[key]; if (typeof value === 'object' && value !== null) { if (Array.isArray(value)) { if (value.length == 1 && typeof value[0] == "string" && value[0].match(regex)) { obj[key] = getPlaceholder(value[0], placeholderMap) } else { for (let i = 0; i < value.length; i++) { if (typeof value[i] === 'object' && value[i] !== null) { traverseAndReplace(value[i]); } else { if (typeof value[i] == "string" && value[i].match(regex)) { value[i] = getPlaceholder(value[i], placeholderMap) } } } } } else { traverseAndReplace(value); } } else { if (typeof value == "string" && value.match(regex)) { obj[key] = getPlaceholder(value, placeholderMap) } } } } const deepCopy = JSON.parse(JSON.stringify(obj)); traverseAndReplace(deepCopy) return deepCopy }32:Ta74,async function* completionLoop(messages, functions, handleCall, gptKey, model=undefined, temperature=undefined, useArgAgents=true) { try { const MAX_QUERIES = 10 const placeholderMap = new Map() let previousFunctionCall = null let queryBody = null for (let i = 0; i < MAX_QUERIES; i++) { const sendMessages = messages.filter(d => !d.displayMessage) queryBody = constructCompletionBody(sendMessages, functions, model, temperature) const queryResult = await completions(queryBody, gptKey) console.log(queryResult) if (queryResult.error) { throw new Error( `Error with query ${i}: ${queryResult.error.message}`) } const choice = queryResult.choices[0] messages.push(choice.message) if (choice.finish_reason == "function_call") { const call = {...choice.message.function_call} call.arguments = await resolveArguments(JSON.parse(call.arguments), useArgAgents) choice.message.function_call.arguments = JSON.stringify(call.arguments)// Ensure argument replacements shown. const expandedCall = replacePlaceholders(call, placeholderMap) const functionResult = await handleCall(expandedCall, placeholderMap) let returnResult = functionResult if (functionResult.placeholder) { returnResult = JSON.stringify(functionResult.placeholder) placeholderMap.set(functionResult.placeholder.dataset, functionResult) for (const [k,v] of Object.entries(functionResult.placeholder.columns)) { placeholderMap.set(k ,v) } } else { returnResult = functionResult.value } previousFunctionCall = call if (!(typeof returnResult == 'string')) returnResult = JSON.stringify(returnResult) messages.push({role: "function", name: call.name, content: returnResult}) let done = false if (previousFunctionCall.name == "showDatasets") { messages.push({role: "assistant", content: "I have displayed the results."}) messages.push({displayMessage: true, content: handleDisplay(previousFunctionCall, placeholderMap)}) done = true } // yield {messages, done: false} if (done) { yield {messages, done: true} break } else { yield {messages, done: false} } } else if (choice.finish_reason == "stop") { yield {messages, done: true} break } } } catch (e) { console.error(e) messages.push({role: "system", content: "An error occurred: " + e.message}) yield {messages, done: true} } }33:T64a,async function gptResolveArgument(key, value) { const prompts = { ingredient: "You turn drug ingredient names into the best matching standard RxNorm name. Respond only with the corrected ingredient name.", disease: "You turn disease names into their best matching MeSH name. Respond only with the corrected disease name.", doseFormGroup: `You turn dose form groups into the best matching RxNorm dose form group (e.g. "oral spray product"). Respond only with the corrected dose form group. Valid answers are: topical product, inhalant product, medicated pad or tape, vaginal product, injectable product, rectal product, mucosal product, nasal product, oral product, dental product, pill, otic product, ophthalmic product, drug implant product, oral liquid product, intraperitoneal product, irrigation product, paste product, urethral product, buccal product, lozenge product, mouthwash product, oral ointment product, oral cream product, sublingual product, toothpaste product, granule product, oral foam product, oral paste product, oral powder product, wafer product, disintegrating oral product, pellet product, shampoo product, chewable product, oral spray product, soap product, oral gel product, transdermal product, intratracheal product, oral film product, pyelocalyceal product ` } const prompt = prompts[key] const completion = await completions(constructCompletionBody([ {role: "system", content: prompt}, {role: "user", content: value}, ], null, "gpt-3.5-turbo-0613", 0), localStorage.GPT_API_key) return completion.choices[0].message.content.toLowerCase() }34:T86e,function getDrugs(args) { args = { drugType: "brand_all", ...args } // Argument checking const productProperties = new Set(["doseFormGroup", "onPatent"]) if (args.drugType == "ingredient" && [...Object.keys(args)].some(d => productProperties.has(d))) { const violatingProperties = [...Object.keys(args)].filter(d => productProperties.has(d)) console.warn(`Cannot use arguments [${violatingProperties.join(", ")}] with drugType 'ingredient', changing drugType to 'brand_all'.`) args.drugType = "brand_all" } let drugs = null if (args.treats) { drugs = ingredientTreats .filter(aq.escape(d => strCompare(d.diseaseName, args.treats))) .select(aq.not(["diseaseCode", "diseaseName"])) } if (args.drugType == "ingredient") { return drugs } drugs = drugs.join(ingredientProduct, "ingredientRxcui") .join(productNames, "productRxcui") if (args.doseFormGroup) { let doseFormGroups = new Set(args.doseFormGroup) drugs = drugs.join(productDoseFormGroup, "productRxcui") .filter(aq.escape(d => doseFormGroups.has(d.dfgName))) .groupby(aq.not(["dfgRxcui", "dfgName"])) .rollup({dfgName: aq.op.array_agg("dfgName")}) } if (args.onPatent != null) { // TODO: We may be able to support ingredient patents if we use the substance flag. This will need to be moved up, and the field will need to be removed from productProperties drugs = drugs.join_left(productPatentExpires, "productRxcui") const date = new Date() if (args.onPatent) { drugs = drugs.filter(aq.escape(d => d.patentExpireDate > date)) } else { drugs = drugs.filter(aq.escape(d => d.patentExpireDate == null || d.patentExpireDate < date)) } } drugs = drugs.join(productNdc, "productRxcui") .join(medicaidProducts.select("ndc", "fdaProductName", "labelerName", "drugCategory"), "ndc") // TODO: Decide what to do with "S" category. if (args.drugType == "brand_innovator") { drugs = drugs.filter(d => d.drugCategory == "I") } else if (args.drugType == "brand_alternative") { drugs = drugs.filter(d => d.drugCategory == "N") } return drugs }35:T6b3,async function getDrugPrices(args) { console.log("getDrugPrices", args) const ndcSet = new Set(args.ndc) //return drugPrices //let prices = liveMedicaidData ? : drugPrices.filter(aq.escape(d => ndcSet.has(d.ndc))) let startDate = luxon.DateTime.now().minus({years: 1}) let endDate = luxon.DateTime.now() function parseDate(dateStr) { try { if (dateStr.startsWith("+")) { dateStr = dateStr.replace("+", "").trim() return luxon.DateTime.now().plus(luxon.Duration.fromISO(dateStr)) } else if (dateStr.startsWith("-")) { dateStr = dateStr.replace("-","").trim() return luxon.DateTime.now().minus(luxon.Duration.fromISO(dateStr)) } else { return luxon.DateTime.fromISO(dateStr) } } catch (e) { console.error(`Error parsing date argument ${dateStr}: ${e.message}`) return null } } if (args.startDate == "latest" || args.endDate == "latest") { // TODO: Implement more. } else { if (args.startDate) { startDate = parseDate(args.startDate) } if (args.endDate) { const endDate = parseDate(args.endDate) } } let prices = null if (liveMedicaidData) { const years = [] for (let year = startDate.year; year <= endDate.year; year++) { years.push(String(year)) } prices = await medicaidDrugPrices([...ndcSet], years) } else { prices = drugPrices.filter(aq.escape(d => ndcSet.has(d.ndc))) } prices = prices.filter(aq.escape(d => d.effective_date >= startDate)) prices = prices.filter(aq.escape(d => d.effective_date <= endDate)) prices = prices.derive({price_per_unit: aq.escape(d => parseFloat(d.price_per_unit))}) return prices }36:T65b,functions = [ { name: "getDrugs", description: `Get drug NDCs and other information. If the user asks for the prices of a 'generic', they mean a 'brand_alternative'. If they ask for 'name brand' or 'brand_name' that is 'brand_innovator'. Dose form groups are standard UMLS groups, e.g. 'Chewable Product'`, parameters: { type: "object", properties: { drugName: {type: "string"}, treats: {type: "string"}, onPatent: {type: "boolean"}, doseFormGroup: {type: "array", items: {type: "string"}}, drugType: {type: "string", enum: ["ingredient", "brand_innovator", "brand_alternative", "brand_all"]}, }, } }, { name: "getDrugPrices", parameters: { type: "object", description: "Takes drug NDCs and returns their prices. ", properties: { ndc: {type: "array", items: {type: "string"}}, startDate: {type: "string"}, endDate: {type: "string"}, minPrice: {type: "number"}, maxPrice: {type: "number"}, priceCategory: {type: "string", enum: ["cheap", "expensive", "increasing", "decreasing"]}, }, }, }, { name: "showDatasets", description: "Display the given dataset(s)", parameters: { type: "object", properties: { datasets: {type: "array", items: {type: "string"}}, } } }, // { // name: "compareDatasets", // description: "Compare the given datasets", // parameters: { // type: "object", // properties: { // datasets: {type: "array", items: {type: "string"}}, // } // } // } ]37:[] 2f:{"id":"0a3e0e60905e6149","slug":null,"title":"MedicaidGPT: Initial Prototype","thumbnail":"6e75ee77108358d305e43561a5c024624050b30b1644d8e595964e355766ee83","access_level":"public","create_time":"$D2023-10-10T19:45:29.679Z","update_time":"$D2023-10-11T16:16:27.994Z","view_time":null,"first_public_time":"$D2023-10-10T19:45:29.679Z","fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"67a652fa41ec3550","login":"aaronge-2020","name":"Aaron Ge","avatar_url":"https://avatars-next.observableusercontent.com/d8cc425d5e64ef116e24ef6e701909b44669b7a3088a311d2de0215ac50324e2","tier":"starter_2024"},"version":716,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"152d0a566a39ccad","login":"aaronge-2020","name":"Aaron Ge","avatar_url":"https://avatars-next.observableusercontent.com/d8cc425d5e64ef116e24ef6e701909b44669b7a3088a311d2de0215ac50324e2","tier":null}],"members":[{"type":"individual","id":"152d0a566a39ccad","login":"aaronge-2020","name":"Aaron Ge","avatar_url":"https://avatars-next.observableusercontent.com/d8cc425d5e64ef116e24ef6e701909b44669b7a3088a311d2de0215ac50324e2","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"MedicaidGPT: Initial Prototype","cells":[{"id":0,"value":"# MedicaidGPT: Initial Prototype","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":516,"value":"## Introduction \n\nMedicaidGPT is an experiment - we aim to provide a tool that allows users to make queries related to wholesale drug pricing, in natural language. MedicaidGPT uses GPT to interpret the queries, the results are displayed in basic interactive visualizations.\n\n## MedicaidGPT Prototype\n\n**Note:** This is a prototype version of MedicaidGPT, if you encounter any bugs please leave a comment on this Observable Notebook.\n\nTo begin, you'll need to provide an OpenAI key. Learn more [here](https://openai.com/blog/openai-api), or go [here](https://platform.openai.com/account/api-keys) to make a new key (requires an OpenAI account).\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":518,"value":"viewof gptKey = {\n return Inputs.password({label: \"GPT Key\", submit: true, width: 640, password: true, required: false})\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":643,"value":"Enter queries into the chat-box below, and click submit. If context is no longer required, use the 'Clear' button to clear previous messages (we recommend this to save on tokens!). ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":641,"value":"chatContainer = htl.html`

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":645,"value":"input = basicChatInput2({value: \"Show me prices of drugs which treat asthma, since February 2022\", submitListener: submitQuery, clearListener: clear})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":629,"value":"### Settings","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":631,"value":"viewof model = Inputs.select([\"gpt-3.5-turbo-0613\", \"gpt-4-0613\"], {label: \"Model\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":634,"value":"viewof temperature = Inputs.range([0, 2], {label: \"Temperature\", value: 0})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":637,"value":"viewof useArgAgents = Inputs.toggle({label: \"Use Argument Agents\", value: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":713,"value":"viewof liveMedicaidData = Inputs.toggle({label: \"Live Medicaid Data\", value: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":564,"value":"## Working","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":666,"value":"{\n const preMessages = gptKey ? [{role: \"system\", content: systemPrompt}] : [{role: \"assistant\", content: \"You must provide an OpenAI key before starting.\"}]\n\n if (!gptKey) {\n input.setEnabled(false)\n } else {\n input.setEnabled(true)\n }\n\n clear()\n chatDisplay(preMessages, null, 600, chatContainer)\n return preMessages\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":647,"value":"async function submitQuery(query, inputElement) {\n if (!gptKey) return \n \n inputElement.setEnabled(false)\n messages.messages.push({role: \"user\", content: query})\n const generator = await completionLoop(messages.messages, functions, handleCall, gptKey, model, temperature, useArgAgents)\n let generatorResponse = await generator.next()\n \n while (!generatorResponse.done) {\n chatDisplay(messages.messages, null, 600, chatContainer)\n generatorResponse = await generator.next()\n }\n inputElement.setEnabled(true)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":659,"value":"function clear() {\n messages.messages = [{role: \"system\", content: systemPrompt}] \n chatDisplay(messages.messages, null, 600, chatContainer)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":649,"value":"messages = {\n const initialMessages = [\n {role: \"system\", content: systemPrompt},\n ]\n return {messages: initialMessages}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":614,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":626,"value":"function handleDisplay(call, placeholderMap) { \n if (call.name == \"showDatasets\") {\n const datasetNames = call.arguments.datasets.slice(-1)\n \n for (const datasetName of datasetNames) {\n const placeholder = getPlaceholder(datasetName, placeholderMap)\n if (datasetName.startsWith(\"getDrugs_\")) {\n const tablesDiv = htl.html`

`\n tablesDiv.appendChild(Inputs.table(expandColumns(placeholder.values)))\n return tablesDiv\n } else if (datasetName.includes(\"getDrugPrices_\")) {\n const data = expandColumns(placeholder.values)\n return (element) => {\n return plotPriceDashboard(element, data)\n }\n }\n \n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":624,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":652,"value":"async function handleCall(call, placeholderMap) {\n if (call.name == \"getDrugs\") {\n const table = getDrugs(call.arguments)\n const placeholdered = placeholderize(\"getDrugs\", table, placeholderMap)\n placeholdered.placeholder.columns = {ndc: placeholdered.placeholder.columns.ndc} // TODO: Remove.\n return placeholdered\n } else if (call.name == \"getDrugPrices\") {\n const table = await getDrugPrices(call.arguments)\n const placeholdered = placeholderize(\"getDrugPrices\", table, placeholderMap)\n placeholdered.placeholder.columns = {ndc: placeholdered.placeholder.columns.ndc} // TODO: Remove.\n return placeholdered\n } else if (call.name == \"showDatasets\" || call.name == \"compareDatasets\") {\n return {value: {status: \"done\"}}\n } else {\n throw Error(\"Unrecognized function call \" + call.name)\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":654,"value":"function placeholderize(name, table, placeholderMap) {\n const columns = {}\n const keys = table.columnNames()\n for (const key of keys) {\n columns[key] = table.array(key)\n }\n \n const placeholders = {}\n keys.forEach(k => {\n const str = placeholderStr(k)\n placeholders[k] = [str]\n placeholderMap.set(str, columns[k])\n })\n \n return {values: columns, placeholder: {dataset: placeholderStr(name), columns: placeholders}}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":597,"value":"### GPT ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":599,"value":"function constructCompletionBody(messages, functions, model=\"gpt-3.5-turbo-0613\", temperature=0.7){\n const postBody = {\n model:model,\n temperature,\n messages,\n }\n\n if (functions != null) {\n postBody.functions = functions\n }\n \n return postBody\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":607,"value":"async function completions(body, gptKey) {\n console.log(\"[Completion]: \", body)\n const response = await \n (await fetch(`https://api.openai.com/v1/chat/completions`,\n {\n method:'POST',\n headers:{\n 'Authorization':`Bearer ${gptKey}`,\n 'Content-Type': 'application/json',\n },\n body:JSON.stringify(body)\n })\n ).json()\n\n return response\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":586,"value":"### Argument Correction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":595,"value":"async function resolveArgument(key, value, useAgent=false) {\n const namespaceValues = dictionary[key]\n\n if (namespaceValues.has(value.toLowerCase())) {\n return value.toLowerCase() \n }\n\n if (useAgent) {\n value = await gptResolveArgument(key, value)\n\n if (namespaceValues.has(value.toLowerCase())) {\n return value.toLowerCase() \n }\n }\n\n const substringMatches = [...namespaceValues].filter(ref => ref.includes(value))\n if (substringMatches.length > 0) {\n return substringMatches\n }\n\n return closestMatch(value, namespaceValues)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":593,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":591,"value":"async function resolveArguments(args, useAgent=false) {\n const namespaceMap = new Map([\n [\"drugName\", \"ingredient\"],\n [\"treats\", \"disease\"],\n [\"doseFormGroup\", \"doseFormGroup\"]\n ])\n\n const argsCopy = JSON.parse(JSON.stringify(args))\n for (const [k,v] of Object.entries(args)) {\n if (namespaceMap.has(k)) {\n if (Array.isArray(v)) {\n argsCopy[k] = await Promise.all(v.map(d => resolveArgument(namespaceMap.get(k), d, useAgent)))\n argsCopy[k] = [...new Set(argsCopy[k])]\n } else {\n argsCopy[k] = await resolveArgument(namespaceMap.get(k),v, useAgent)\n }\n }\n }\n\n return argsCopy\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":588,"value":"dictionary = ({\n ingredient: new Set(ingredientNames.columnArray(\"name\")),\n disease: new Set(ingredientTreats.columnArray(\"diseaseName\").map(d => d.toLowerCase())),\n doseFormGroup: new Set(doseFormGroupNames.columnArray(\"name\").map(d => d.toLowerCase())),\n\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":568,"value":"### Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":571,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":583,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":708,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":707,"value":"medicaidDatasets = jfetch('https://data.medicaid.gov/api/1/metastore/schemas/dataset/items?show-reference-ids=false')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":706,"value":"drugCostDatasetMap = d3.index(medicaidDatasets.filter(d => d.title.includes(\"Cost\")), d => d.title.split(\" \").at(-1))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":705,"value":"async function medicaidDrugPrices(ndcs, years) {\n const allPrices = []\n for (const year of years) {\n const datasetId = drugCostDatasetMap.get(year).distribution[0].identifier\n \n const prices = await (await makePostRequest(`https://data.medicaid.gov/api/1/datastore/query/${datasetId}`, {\n \"conditions\": [\n {\n \"resource\": \"t\",\n \"property\": \"ndc\",\n \"value\": ndcs,\n \"operator\": \"in\"\n }\n ],\n \"limit\": 10000\n })).results\n\n prices.forEach(row => allPrices.push(row))\n }\n return aq.from(allPrices)\n .rename({ndc_description: \"drug_name\", nadac_per_unit: \"price_per_unit\"})\n .select([\"ndc\", \"drug_name\", \"effective_date\", \"price_per_unit\"])\n .derive({\n effective_date: aq.escape(d => new Date(d.effective_date))\n })\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":575,"value":"### Helper ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":620,"value":"function expandColumns(columns) {\n const objects = []\n\n for (let i = 0; i < [...Object.values(columns)][0].length; i++) {\n const obj = {}\n for (const [k,v] of Object.entries(columns)) {\n obj[k] = columns[k][i]\n }\n objects.push(obj)\n }\n\n return objects\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":616,"value":"function getPlaceholder(name, placeholderMap) {\n if (!name.startsWith(\"{\")) {\n name = `{${name}}`\n }\n return placeholderMap.get(name, placeholderMap)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":577,"value":"function strCompare(a,b) {\n return a.toLowerCase() == b.toLowerCase()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":604,"value":"function closestMatch(str, strs) {\n const matches = [...strs].map(d => ({string: d, distance: lv.get(str, d)}))\n matches.sort((a,b) => a.distance - b.distance)\n return matches[0].string\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":656,"value":"function placeholderStr(name) {\n return `{${name}_${nanoid.nanoid(6).replaceAll(\"_\",\"0\")}}`\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":704,"value":"async function jfetch(url) {\n return (await fetch(url)).json()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":703,"value":"async function makePostRequest(url, requestBody) {\n const options = {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json' \n },\n body: JSON.stringify(requestBody)\n }\n\n return (await fetch(url, options)).json()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":554,"value":"## GPT Specification","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":557,"value":"systemPrompt = `You are an assistant that will help the user find information about drugs and their pricing.\n\nFunction responses in curly braces are placeholders, and should be used in subsequent function calls where appropriate.\n\nIMPORTANT: Do not give absolute dates unless the user asks for them specifically. If the user asks for relative dates, provide them as an ISO 8601 duration, prepended with a '-' or '+, e.g. 3 years ago would be '-P3Y'\n\nIMPORTANT: Don't attempt to summarise yourself, call the 'showDatasets' or 'compare' functions. Only show the minimal \ndatasets necessary to answer the user's question (one is usually enough). If the user asks information that requires multiple datasets, then get all the information first and call 'showDatasets' or 'compareDatasets' at the end.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":560,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":521,"value":"## Style","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":523,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":526,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":529,"value":"faStyle({regular: true, solid: true, brands: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":531,"value":"## Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":573,"value":"import { ingredientTreats, ingredientProduct, productNames, productDoseFormGroup, productPatentExpires, productNdc, medicaidProducts, drugPrices, ingredientNames, doseFormGroupNames, luxon } from \"@siliconjazz/druggpt-function-functionality-playground\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":533,"value":"import { basicChatInput2, getBoxStyle, chatDisplay } from \"@siliconjazz/basic-chat-display\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":536,"value":"import {plotPriceDashboard} from \"@siliconjazz/medicaidgpt-plots\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":539,"value":"nanoid = import('https://cdn.skypack.dev/nanoid@4.0.2?min')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":541,"value":"lv = (await import('https://cdn.skypack.dev/fast-levenshtein@3.0.0?min')).default","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":543,"value":"import { style as faStyle, fa } from \"@airbornemint/fontawesome\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W37","stdlib":"1","titling":"auto"}} 30:"$2f:body"